The invention is related to the field of neuromotor prosthetics, and in particular an algorithm for continuous-time linear decoding and learning in neuromotor prosthetics.
In a set of amazing experiments, several groups in the world have now proven that the dream of enabling paralyzed patients to move paralyzed limbs is well within reach. The majority of these experiments have been done in rats or monkeys, although a company, Cyberkinetics Inc., has demonstrated that a paralyzed patient can control a mouse on a computer screen merely by thinking about doing so. However, current neuromotor prosthetics are extremely bulky and power-hungry and are not practical for use in human patients. The system used by Cyberkinetics, for example, requires a full-sized processing computer to be mounted on the wheel chair of the patient and bulky recording electronics to be mounted on the patient's head. Smaller-size portable neural recording has been implemented, but the discrete electronics used require high-power operation and would need further processing to implement an algorithm to decode the intention of the monkey to move. Next-generation neuromotor prosthetics will be small and or fully implanted in the patient's brain, imposing a stringent requirement on power consumption due to the need for small size, long battery life, and minimum heat dissipation in the brain and skull. Power-efficient algorithm and electronic design can make portability and chronic usage of neuromotor prosthetics in real patients a reality.
One major concern in the design of a neuromotor prosthetic system is the power consumption in the digitization of raw neural signals (at 10 bit precision and 20 kHz bandwidth) and in the wireless communication circuitry for transmitting digitized neural data out of the brain (20 Mbs−1 for 100 neural channels). The power costs of both the wireless communication and raw neural signal digitization can be significantly reduced if an analog network is used to preprocess the information such that low-precision, low bandwidth information is communicated out of the brain, thus saving power in digitization, communication, and digital post-processing of the communicated information. For the typically low bandwidths and precisions needed at the output of a neuromotor prosthetic (a 10 ms response time on the actuator controls at best, 8 bits of precision, and 3 motor output dimensions), an analog network that is capable of computing 3 motor outputs from 100 analog neural signals can enable a significant reduction in the communicated data bandwidth from about 20 Mbs−1 to 2.4 kbs−1 and a significant reduction in the overall system power.
As an example, analog preprocessing could enable more than an order of magnitude reduction in power in cochlear-implant processors by enabling digitization of output spectral information for driving electrodes rather than immediate digitization and digital signal processing of raw sound data from a microphone. That processor was also programmable with 373 bits enabling a change of 86 chip parameters. It was robust to power-supply-noise at RF frequencies and temperature variations because of the use of noise-robust biasing techniques.
The use of an analog network for preprocessing to achieve drastic data reduction is beneficial in lowering power in other schemes that have been implemented as well: For example, systems with multichannel wireless telemetry of threshold spikes could be adapted to reduce their power requirements by lowering their digitization and telemetry costs with a scheme such as ours for prosthetic applications. Analog processing is particularly advantageous in slow-and-parallel applications like neuromotor prosthetics where the final output bandwidth and needed precision for the task are relatively modest and involve significant data reduction. In such applications, the noise and offset in an analog system may be managed efficiently to preserve the needed output precision.
A variety of decoding techniques have been developed and implemented successfully in rodents, monkeys, and humans. Major commonalities among the decoding methods employed in these systems have been reviewed in the literature, and include two primary strategies: adaptive linear filtering and probabilistic methods. Thus far, all of these techniques have been proposed for discrete-time digital implementations. In spite of dramatic preliminary successes reported in the field of neuromotor prosthetics, all existing systems accomplish neural decoding through the use of massive amounts of signal-processing hardware and digital post processing.
A highly sophisticated decoding algorithm is not necessarily more beneficial in the long run because the brain is adept at learning and compensating for errors in the decoding algorithm if sensory feedback is present. Learning is nevertheless important in the decoding algorithm to ensure that performance does not degrade over time due to the loss of certain neural signals via electrode degradation which can be compensated for by the brain by using other functional neural signals in the array, and to adapt to the slow variability of the recordings.